Rivers, one of the most complex ecosystems are highly dynamic and vary spatially as well as temporally. Chlorophyll-a (Chl-a) is considered one of the primary indicators of water quality and a measure of river productivity, while turbidity in rivers is a measure of suspended organic matter. Monitoring of river water quality is quite challenging, demand tremendous efforts and resources. Numerous algorithms have been developed in the recent years for estimating environmental parameters such as chlorophyll-a and turbidity from remote sensing imagery. However, most of these algorithms were focused on the lentic ecosystems. There is a paucity of algorithms for rivers from which water quality variables can be estimated using remotely sensed imagery. The primary objective of our study is to develop algorithms based on Landsat 8 OLI imagery and in-situ observations for estimating of Chl-a and turbidity in the Upper Ganga river, India. Band reflectance images from multispectral Landsat-8 OLI pertaining to May and October 2016, and May 2017 were used for model development and validation along with near synchronous ground truth data. Algorithms based on Band 3 (R<sup>2</sup>= 0.73) proved to be the best applicable algorithm for estimating chlorophyll-a. The best algorithm for estimating turbidity was found to be log (B4/B5) (R<sup>2</sup>= 0.69) based on band combinations (individual band reflectance, band ratio, logarithmically transformed band reflectance and ratios) tested. The developed algorithms were used to generate maps showing the spatiotemporal variability of chlorophyll-a and turbidity concentration in the Upper Ganga river (Brijghat to Narora) which is also a Ramsar site.

Wetlands, one of the most productive ecosystems on Earth, perform myriad ecological functions and provide a host of ecological services. Despite their ecological and economic values, wetlands have experienced significant degradation during the last century and the trend continues. Hyperspectral sensors provide opportunities to map and monitor macrophyte species within wetlands for their management and conservation. In this study, an attempt has been made to evaluate the potential of narrowband spectroradiometer data in discriminating wetland macrophytes during different seasons. main objectives of the research were (1) to determine whether macrophyte species could be discriminated based on in-situ hyperspectral reflectance collected over different seasons and at each measured waveband (400-950nm), (2) to compare the effectiveness of spectral reflectance and spectral indices in discriminating macrophyte species, and (3) to identify spectral wavelengths that are most sensitive in discriminating macrophyte species. Spectral characteristics of dominant wetland macrophyte species were collected seasonally using SVC GER 1500 portable spectroradiometer over the 400 to 1050nm spectral range at 1.5nm interval, at the Bhindawas wetland in the state of Haryana, India. Hyperspectral observations were pre-processed and subjected to statistical analysis, which involved a two-step approach including feature selection (ANOVA and KW test) and feature extraction (LDA and PCA). Statistical analysis revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from visible to the near-infrared regions. The results suggest that hyperspectral data can be used discriminate wetland macrophyte species working as an effective tool for advanced mapping and monitoring of wetlands.

Hyperspectral remote sensing has shown great promise in characterizing and monitoring of optical properties of water. This study aims at characterizing the spectral reflectance and to develop hyperspectral algorithms for retrieval of bio-optical properties of Bhindawas wetland, a man-made lake in Haryana, India. The spectral reflectance of the lake was measured using SVC GER 1500 Spectroradiometer and water samples were collected from different sampling sites within the lake during three different field surveys in 2014. Characterization of spectral responses was carried out using principal component analysis and Canonical Correspondence Analysis (CCA). It revealed that the dataset was typical of Case II waters by extracting two principal components that explained around 99% of the variation, and CCA identified that different optical parameters such as TSS, TOC, Chla and turbidity delineate its spectral response. Water quality results were correlated with reflectance to determine their relationships. Furthermore, multiple linear regression was used to derive the two and three band model for retrieval of TSS, Chla and Turbidity concentration for lake. Retrieval algorithms with significant accuracy were developed for Chla (R<sup>2</sup>=0.80, RMSE=0.12&mu;g/l), TSS (R2=0.86, RMSE=59.1mg/l) and Turbidity (R<sup>2</sup>=0.84, RMSE=13NTU).

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Keywords/Phrases

Keywords

in

Remove

in

Remove

in

Remove

+ Add another field

Search In:

Proceedings

Volume

Journals +

Volume

Issue

Page

Advanced PhotonicsJournal of Applied Remote SensingJournal of Astronomical Telescopes Instruments and SystemsJournal of Biomedical OpticsJournal of Electronic ImagingJournal of Medical ImagingJournal of Micro/Nanolithography, MEMS, and MOEMSJournal of NanophotonicsJournal of Photonics for EnergyNeurophotonicsOptical EngineeringSPIE Reviews